{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Keys"
      ],
      "metadata": {
        "id": "uFd1P-2jOTme"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"WANDB_API_KEY\"] = \"YOUR_KEY\""
      ],
      "metadata": {
        "id": "KuDR9AmpORDF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0rYXLkwpLcOB"
      },
      "source": [
        "# Our First Reinforcement Learning from Feedback Loop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4nv5wwZnL1Kh"
      },
      "outputs": [],
      "source": [
        "!pip install wandb datasets transformers tqdm trl"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s7Woq9XmLcOC"
      },
      "source": [
        "### Import dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UZKZHwNMLcOC"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "import wandb\n",
        "import time\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from random import choices\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "tqdm.pandas()\n",
        "\n",
        "from datasets import load_dataset\n",
        "\n",
        "from transformers import AutoTokenizer, pipeline\n",
        "\n",
        "from trl import PPOTrainer, PPOConfig, create_reference_model, AutoModelForSeq2SeqLMWithValueHead\n",
        "import os\n",
        "import torch\n",
        "\n",
        "import os\n",
        "\n",
        "\n",
        "wandb.login(key=os.environ['WANDB_API_KEY'])\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hTTC06W6LcOD"
      },
      "source": [
        "# The Reward Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194,
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        },
        "id": "dwkZl3tZLcOD",
        "outputId": "7ea459c3-9472-424a-8f26-adf7cd13d882"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/747 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/499M [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/150 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2815b7823b1483fab79ceb2f861c4f4"
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          },
          "metadata": {}
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.851918637752533, -0.7468027472496033, -0.5696872472763062]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "sentiment_pipeline = pipeline('text-classification', 'cardiffnlp/twitter-roberta-base-sentiment')\n",
        "\n",
        "def get_neutral_scores(texts):\n",
        "    scores = []\n",
        "    # function_to_apply='none' returns logits which can be negative which is great for RL rewards\n",
        "    results = sentiment_pipeline(texts, function_to_apply='none', top_k=None)\n",
        "    for result in results:\n",
        "        for label in result:\n",
        "            if label['label'] == 'LABEL_1':  # logit for neutral class\n",
        "                scores.append(label['score'])\n",
        "    return scores\n",
        "\n",
        "get_neutral_scores(['hello', 'I love you!', 'I hate you'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mPePmvGLLcOD"
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      "outputs": [],
      "source": []
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        "colab": {
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        "id": "Mi6qGmvmLcOD",
        "outputId": "e276ea9b-98ae-49ee-c8ad-b0d0ed6251cd",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3abfd9d0919a4319b3bb68a8284151a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/501M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d6f5a9413554b89a3579b285b0f82f5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at textattack/roberta-base-CoLA were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"textattack/roberta-base-CoLA\")\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"textattack/roberta-base-CoLA\")\n",
        "\n",
        "cola_pipeline = pipeline('text-classification', model=model, tokenizer=tokenizer)\n",
        "\n",
        "def get_cola_scores(texts):\n",
        "    scores = []\n",
        "    # note function_to_apply='none' gives me logits which can be negative (what we want)\n",
        "    results = cola_pipeline(texts, function_to_apply='none', top_k=None)\n",
        "    for result in results:\n",
        "        for label in result:\n",
        "            if label['label'] == 'LABEL_1':  # good grammar:\n",
        "                scores.append(label['score'])\n",
        "    return scores\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kA5IyXmiLcOD",
        "outputId": "ffb1b1ab-7937-40d6-dc8f-be1b9026097e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.38008707761764526]\n",
            "[0.3111610412597656]\n"
          ]
        }
      ],
      "source": [
        "test = \"\"\"German police arrest 29-year-old man suspected of plotting truck attack on ice rink in Berlin,\n",
        "killing 12 people, Reuters-German police say: 'It's a shame to have been arrested,'\n",
        "a German police spokesman said in a statement.\"\"\"\n",
        "\n",
        "print(get_cola_scores([test]))\n",
        "print(get_neutral_scores([test]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upug4DOsLcOE"
      },
      "outputs": [],
      "source": [
        "MODEL_NAME = \"google/flan-t5-small\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njmhdWttLcOE"
      },
      "source": [
        "# RL Configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "whXv7P5MLcOE"
      },
      "outputs": [],
      "source": [
        "config = PPOConfig(\n",
        "    model_name=MODEL_NAME,\n",
        "    batch_size=8,\n",
        "    learning_rate=2e-5,\n",
        "    remove_unused_columns=False,\n",
        "    log_with=\"wandb\",\n",
        "    gradient_accumulation_steps=4,\n",
        ")\n",
        "\n",
        "np.random.seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MY6gpraoLcOE"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 273,
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        },
        "id": "_N2ecIvHLcOE",
        "outputId": "cd248403-3201-4b3f-a916-e7ae5fbfc82f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f30f850a9d8841a1842f492615d95347"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/308M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71d84ef4c6ca41a6b57138565ac0f8a4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0c25e3303a6409b91fd63b3bcfc7cd9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/308M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "600a2d1c3c4b4eb0b69ad1ec36d77538"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d68127774cdb4db993025ae82290db42"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e5d2248c7004ea386923525dad645c7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f68725ec162346cf8de68a4dda75434d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "flan_t5_model = AutoModelForSeq2SeqLMWithValueHead.from_pretrained(\n",
        "    config.model_name\n",
        ")  # going to be updated\n",
        "\n",
        "flan_t5_model_ref = create_reference_model(flan_t5_model)  # is never updated\n",
        "flan_t5_tokenizer = AutoTokenizer.from_pretrained(config.model_name)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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            "3cfe8a62bd234df485474ee62edf0921"
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        },
        "id": "l7SMO8HTLcOE",
        "outputId": "ec98f6d7-e642-4933-feb1-81598f899860",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/2.02k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "253afbae8b4245a3845a3c666ac0f783"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.54M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d4d4a9b3f494094bb4303756c4026b7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/31.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0eac95f7d61d454b9d3e3ac194099cd1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3b8fac678974b05925e3e86521b956f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/20417 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3a39dc0ccb224c5faa3debca5cbd53f8"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"argilla/news-summary\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZI8vX5pLcOE",
        "outputId": "ea05ae3e-e8ba-4d2e-b2d9-b52cb36b5ff1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'prediction', 'prediction_agent', 'annotation', 'annotation_agent', 'id', 'metadata', 'status', 'event_timestamp', 'metrics'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'prediction', 'prediction_agent', 'annotation', 'annotation_agent', 'id', 'metadata', 'status', 'event_timestamp', 'metrics'],\n",
              "        num_rows: 20417\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X-t_usDCLcOE",
        "outputId": "7e0cb14e-5e5b-4f64-e7e6-19bf58886c94"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'PHNOM PENH (Reuters) - Sweden said on Tuesday it was stopping new aid for Cambodia, except in education and research, and would no longer support a reform programme after the main opposition party was outlawed by the Supreme Court at the government s request. The announcement marked the first concrete action by a European Union country in protest at a political crackdown in which veteran Prime Minister Hun Sen s main rival has also been arrested and civil rights groups and independent media attacked. The United States cut election funding and said it would take more punitive steps after last week s ban on the Cambodia National Rescue Party (CNRP). The European Union has also threatened action. Sweden s embassy in Phnom Penh said the country was reviewing its engagement with Cambodia.   We will not initiate any new government-to-government development cooperation agreements, except in the areas of education and research,  it said in a statement. As a consequence, it would be unable to support decentralisation reform in its current form. That reform aims to strengthen lower levels of government, such as local communes. The CNRP won control of more than 40 percent of the communes in elections in June, but has now had to give them up to the ruling Cambodian People s Party (CPP). The CNRP was banned after its leader, Kem Sokha, was arrested for alleged treason. The government says he sought to take power with American help. He rejects that allegation as politically motivated, to allow Hun Sen to extend his more than three decades in power in next year s general election. Responding to the Swedish statement, a senior official said Cambodia welcomed friendship with Sweden or other countries, but said they must understand the CNRP had been banned because the courts found it had committed treason.      People should respect the Cambodian people s decision in accordance with the principle of democracy and the rule of law,  said Huy Vannak, undersecretary of state at the Interior Ministry. Sweden, which has given Cambodia an estimated $100 million in aid over five years, ranked third among individual EU member states in Cambodia s database of donors last year, after France and Germany. Swedish fashion group H&M is also a key buyer from Cambodia s garment factories - the country s main export earner. But Western donors have less sway than they once did since China has emerged as Cambodia s biggest aid donor and investor. Meeting on Monday on the sidelines of a meeting of Asia-Europe foreign ministers in Myanmar, Chinese Foreign Minister Wang Yi told his Cambodian counterpart Prak Sokhon that China supported the government s actions. China has repeatedly expressed its support for Cambodia, making no criticism of the government led by Hun Sen, a former Khmer Rouge commander, who is one of Beijing s most important allies in Southeast Asia.    (The story adds dropped word  after  in paragraph 1) ',\n",
              " 'prediction': [{'score': 1.0,\n",
              "   'text': 'Sweden stops some new aid for Cambodia in protest over crackdown'}],\n",
              " 'prediction_agent': 'Argilla',\n",
              " 'annotation': None,\n",
              " 'annotation_agent': None,\n",
              " 'id': '04830409-7789-4d9d-ae71-cbde4a5c7bde',\n",
              " 'metadata': None,\n",
              " 'status': 'Default',\n",
              " 'event_timestamp': datetime.datetime(2017, 11, 21, 0, 0),\n",
              " 'metrics': {'text_length': 2933}}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "dataset['train'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "1e5ac52ca1734393b7dabb51438cd64a",
            "d4579761b90b4fc196c5e90066f48a6c",
            "5893fa44f4824843b4b7fb9272df38fc",
            "f88bcdac57994adc89f9f4e496c49a63",
            "4b8cc0abb3cf45b1be163ded03b3faaf",
            "91bf440296ae4485998de3c1af7459d1",
            "8b08c53d6b3049f190380f49434530f7",
            "8da980374ce14783b706b95f86930a9d",
            "4708975d019341afb3fe6ef607a17ae9",
            "92a4eece153947968a5da98f1dc58f3d",
            "418cd1cb206444f2b452764fc351c04b"
          ]
        },
        "id": "lTvclDRJNVka",
        "outputId": "7196c017-f5a1-414b-d58e-d567dc298a39"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e5ac52ca1734393b7dabb51438cd64a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# Preprocessing function\n",
        "def preprocess(batch):\n",
        "    # 'texts' should be a list of text entries within each batch\n",
        "    texts = batch['text']  # assuming the key for your texts in your dataset is 'text'\n",
        "\n",
        "    # Tokenize the texts (appending 'summarize: ' before each)\n",
        "    tokenized_inputs = flan_t5_tokenizer(\n",
        "        ['summarize: ' + t for t in texts],\n",
        "        truncation=True,\n",
        "        padding=True,\n",
        "        return_tensors=\"pt\"\n",
        "    )\n",
        "\n",
        "      # Remove padding\n",
        "    input_ids_list = []\n",
        "    attention_mask_list = []\n",
        "\n",
        "    for idx, input_ids in enumerate(tokenized_inputs['input_ids']):\n",
        "        actual_length = tokenized_inputs['attention_mask'][idx].sum()  # Count number of non-padding tokens\n",
        "        input_ids_list.append(input_ids[:actual_length])\n",
        "        attention_mask_list.append(tokenized_inputs['attention_mask'][idx, :actual_length])\n",
        "\n",
        "    batch['input_ids'] = input_ids_list\n",
        "    batch['attention_mask'] = attention_mask_list\n",
        "\n",
        "    return batch\n",
        "\n",
        "dataset['train'] = dataset['train'].map(\n",
        "    preprocess,\n",
        "    batched=True,\n",
        "    batch_size=256,\n",
        "    remove_columns=set(dataset['train'].column_names) - {'text',}\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ytc4WhDeLcOE",
        "outputId": "ac811687-9f78-42a7-c0c7-9e911f1c49aa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'prediction', 'prediction_agent', 'annotation', 'annotation_agent', 'id', 'metadata', 'status', 'event_timestamp', 'metrics'],\n",
              "        num_rows: 20417\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "dataset.set_format(\"pytorch\")\n",
        "\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uBSHgsIaLcOE"
      },
      "outputs": [],
      "source": [
        "def collator(data):\n",
        "    return dict((key, [d[key] for d in data]) for key in data[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OtY3wdz_LcOF"
      },
      "source": [
        "# Load our reference FLAN-T5 Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0SAXKxpSLcOF"
      },
      "outputs": [],
      "source": [
        "generation_kwargs = {\n",
        "    \"min_length\": 64,  # forcing some behavior I want\n",
        "    \"num_beams\": 5,  # lookahead parameter\n",
        "    \"no_repeat_ngram_size\": 3,  # cannot say the same three n-grams in a row twice\n",
        "    \"do_sample\": True,  # giving the LLM freedom to experiment\n",
        "    \"pad_token_id\": flan_t5_tokenizer.pad_token_id,  # technically not needed in this example, but is if doing PPO in larger batches\n",
        "    \"max_length\": 256,\n",
        "    \"eos_token_id\": flan_t5_tokenizer.eos_token_id,\n",
        "}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P-ZCaJNtLcOF",
        "outputId": "24b90a85-5e72-4c58-e99f-aa16013f47f4",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.15.10\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/content/wandb/run-20230921_160015-l40j1gia\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mefficient-elevator-206\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/profoz/trl\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/profoz/trl/runs/l40j1gia\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "ppo_trainer = PPOTrainer(\n",
        "    config, flan_t5_model,\n",
        "    flan_t5_model_ref, flan_t5_tokenizer, dataset['train'],\n",
        "    data_collator=collator\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E84V17LkLcOF"
      },
      "source": [
        "### Our RLF Loop\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "1e0ba639d01d4d8295a1886fa4248196",
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            "9222c668a0094524a72d36eedb3147ca",
            "e5c93e98e40c4f1f9acb0e688cbec4f1",
            "2c59fac1b2e048eb9bf1788994c95099",
            "66920ac7efce4bf5b220df7377b66a24",
            "37fed5b1b2394b8987addfb2e7799b11",
            "1bafaa46422b4942b237c6680f19c8a5",
            "c5c6b359c03f408f98b25abbbfde1379",
            "f33877f2c2d64ae29438a0995cecc49c",
            "c4285d10cd7049e59e2654fc755c97b3",
            "19ce72e619fe4b108a00feac80585246",
            "7737d750f3d14e1eb825d67a1ceadced",
            "de197f5849d44bbe9f5955cb7d8415b3",
            "8a22a00e4c354990adbe9ddb2ccad09c",
            "510a196015964b54b598e0b461a3d084",
            "5d9bd72d75e34ccfa0dc9ce3969bc28b",
            "022b3d26ca7943a4874896517eea25db",
            "50aec8f6ae584d09b58a5ea3c5e0aeaa"
          ]
        },
        "id": "88AZaik0LcOF",
        "scrolled": true,
        "outputId": "34d4a6de-2569-46b7-9b8d-34bf70bdbbc3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1e0ba639d01d4d8295a1886fa4248196",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1bafaa46422b4942b237c6680f19c8a5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/125 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "50aec8f6ae584d09b58a5ea3c5e0aeaa",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/125 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "for epoch in tqdm(range(2)):\n",
        "    for batch in tqdm(ppo_trainer.dataloader):\n",
        "        game_data = dict()\n",
        "        #### prepend the summarize token\n",
        "        game_data[\"query\"] = ['summarize: ' + b for b in batch[\"text\"]]\n",
        "\n",
        "        #### get response from flan-t5\n",
        "        input_tensors = [_.squeeze() for _ in batch[\"input_ids\"]]\n",
        "        response_tensors = []\n",
        "        for query in input_tensors:\n",
        "            response = ppo_trainer.generate(query.squeeze(), **generation_kwargs)\n",
        "            response_tensors.append(response.squeeze())\n",
        "\n",
        "        game_data[\"response\"] = [flan_t5_tokenizer.decode(r.squeeze(), skip_special_tokens=False) for r in response_tensors]\n",
        "\n",
        "        #### reward system\n",
        "        game_data[\"clean_response\"] = [flan_t5_tokenizer.decode(r.squeeze(), skip_special_tokens=True) for r in response_tensors]\n",
        "        game_data['cola_scores'] = get_cola_scores(game_data[\"clean_response\"])\n",
        "        game_data['neutral_scores'] = get_neutral_scores(game_data[\"clean_response\"])\n",
        "        # rewards = game_data['neutral_scores']\n",
        "        transposed_lists = zip(game_data['cola_scores'], game_data['neutral_scores'])\n",
        "        # Calculate the averages for each index\n",
        "        rewards = [1 * values[0] +  0.5 * values[1] for values in transposed_lists]\n",
        "        rewards = [torch.tensor([_]) for _ in rewards]\n",
        "        # print(rewards)  # just to inspect :)\n",
        "\n",
        "        #### Run PPO training\n",
        "        stats = ppo_trainer.step(input_tensors, response_tensors, rewards)\n",
        "\n",
        "        stats['env/reward'] = np.mean([r.cpu().numpy() for r in rewards])\n",
        "        ppo_trainer.log_stats(stats, game_data, rewards)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tGoU7J9iLcOF",
        "outputId": "d90bbbfd-d6ff-4639-9edc-1d29669463ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('t5-align/tokenizer_config.json',\n",
              " 't5-align/special_tokens_map.json',\n",
              " 't5-align/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# save locally\n",
        "flan_t5_model.save_pretrained(\"t5-align\")\n",
        "flan_t5_tokenizer.save_pretrained(\"t5-align\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "username, repo_name = 'profoz', 't5-aligned-summaries'\n",
        "\n",
        "# Push model and tokenizer to Hugging Face Hub\n",
        "flan_t5_model.push_to_hub(f\"{username}/{repo_name}\")\n",
        "flan_t5_tokenizer.push_to_hub(f\"{username}/{repo_name}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "680d1348126f454f9a165fd23adc9598",
            "a2540784665a4803a6394a687a5b3a56",
            "893ae565c67e48408905ea557f6f3f73",
            "6fd218c6a8e14fbd904f4fdc41ba63b1",
            "149825048060439da7d7548546dcbc00",
            "d285d5a724214f85ba949336df5c17fe",
            "b10fec3000fa4af18d4f7b6b0075ae19",
            "1c301adabf9649159049aef6ea3f0796",
            "d5a406224b3645edbc0f550e3bcd80ff",
            "a3d14591275a4524bbc22747882d6bb8",
            "96ebd71ac89d4dab9f6ee9d6f5105c90"
          ]
        },
        "id": "FR1w51Kd7uZh",
        "outputId": "1e284cfe-749e-44f9-8f56-f4595a86c82d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at t5-align were not used when initializing T5ForConditionalGeneration: ['v_head.summary.bias', 'v_head.summary.weight']\n",
            "- This IS expected if you are initializing T5ForConditionalGeneration from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing T5ForConditionalGeneration from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/308M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "680d1348126f454f9a165fd23adc9598"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/profoz/t5-aligned-summaries/commit/87f8a5d71333a65af0e21e59d4076a684cc243ee', commit_message='Upload tokenizer', commit_description='', oid='87f8a5d71333a65af0e21e59d4076a684cc243ee', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "old_summarizer = pipeline(\"text2text-generation\", MODEL_NAME, device=0)\n",
        "new_summarizer = pipeline(\"text2text-generation\", 'profoz/t5-aligned-summaries', device=0)"
      ],
      "metadata": {
        "id": "d3xpfD7v_iO9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "# Assuming load_dataset function loads the dataset\n",
        "dataset = load_dataset(\"argilla/news-summary\")['test']['text']\n",
        "\n",
        "# Filter dataset for entries with less than 400 characters\n",
        "filtered_dataset = [text for text in dataset if len(text) < 450 and len(text) > 200]\n",
        "\n",
        "filtered_dataset = random.sample(filtered_dataset, len(filtered_dataset))"
      ],
      "metadata": {
        "id": "PWm18uerFZ93"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "old_summaries, new_summaries = [], []\n",
        "# Lists to store scores\n",
        "summary_list, sentiment_scores_old, sentiment_scores_new, cola_scores_old, cola_scores_new = [], [], [], [], []\n",
        "\n",
        "for i, article in enumerate(tqdm(filtered_dataset)):\n",
        "    # Old model\n",
        "    results_old = old_summarizer(f\"summarize: {article}\", **generation_kwargs)\n",
        "    summary_text_old = results_old[0]['generated_text']\n",
        "    old_summaries.append(summary_text_old)\n",
        "\n",
        "    # New model\n",
        "    results_new = new_summarizer(f\"summarize: {article}\", **generation_kwargs)\n",
        "    summary_text_new = results_new[0]['generated_text']\n",
        "    new_summaries.append(summary_text_new)\n",
        "\n",
        "    # if summary_text_new.endswith('.') and '...' not in summary_text_new:\n",
        "    # print(f\"Generated Summary {i+1} [NEW]: {summary_text_new}\")\n",
        "\n",
        "    # Old scores\n",
        "    sentiment_score_old = get_neutral_scores([summary_text_old])\n",
        "    sentiment_scores_old.extend(sentiment_score_old)\n",
        "    cola_score_old = get_cola_scores([summary_text_old])\n",
        "    cola_scores_old.extend(cola_score_old)\n",
        "\n",
        "    # New scores\n",
        "    sentiment_score_new = get_neutral_scores([summary_text_new])\n",
        "    sentiment_scores_new.extend(sentiment_score_new)\n",
        "    # print(f\"Neutral Rewards for Summary {i+1} [NEW]: {sentiment_score_new}\")\n",
        "\n",
        "    cola_score_new = get_cola_scores([summary_text_new])\n",
        "    cola_scores_new.extend(cola_score_new)\n",
        "    # print(f\"Cola Rewards for Summary {i+1} [NEW]: {cola_score_new}\")"
      ],
      "metadata": {
        "id": "80HETV5_pcm0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "gpHmyiDcLcOG",
        "scrolled": true,
        "outputId": "5f4ebfe8-5bda-4ef2-8aa4-abb709f856ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Neutral Reward [OLD]: 1.3064811193536916\n",
            "Average Cola Reward [OLD]: 0.6901628242448794\n",
            "Average Neutral Reward [NEW]: 1.3233293617095314\n",
            "Average Cola Reward [NEW]: 0.7584201722146664\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# Calculate averages\n",
        "average_sentiment_score_old = sum(sentiment_scores_old) / len(sentiment_scores_old)\n",
        "average_cola_score_old = sum(cola_scores_old) / len(cola_scores_old)\n",
        "average_sentiment_score_new = sum(sentiment_scores_new) / len(sentiment_scores_new)\n",
        "average_cola_score_new = sum(cola_scores_new) / len(cola_scores_new)\n",
        "\n",
        "median_sentiment_score_old = np.median(sentiment_scores_old)\n",
        "median_cola_score_old = np.median(cola_scores_old)\n",
        "median_sentiment_score_new = np.median(sentiment_scores_new)\n",
        "median_cola_score_new = np.median(cola_scores_new)\n",
        "\n",
        "std_sentiment_score_old = np.std(sentiment_scores_old)\n",
        "std_cola_score_old = np.std(cola_scores_old)\n",
        "std_sentiment_score_new = np.std(sentiment_scores_new)\n",
        "std_cola_score_new = np.std(cola_scores_new)\n",
        "\n",
        "print(f'Average Neutral Reward [OLD]: {average_sentiment_score_old}')\n",
        "print(f'Average Cola Reward [OLD]: {average_cola_score_old}')\n",
        "print(f'Average Neutral Reward [NEW]: {average_sentiment_score_new}')\n",
        "print(f'Average Cola Reward [NEW]: {average_cola_score_new}')\n",
        "# print('---------------------------')\n",
        "\n",
        "# Graph the differences\n",
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "# Sentiment scores\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.bar([\"Old Mean\", \"New Mean\", \"Old Median\", \"New Median\"],\n",
        "        [average_sentiment_score_old, average_sentiment_score_new, median_sentiment_score_old, median_sentiment_score_new],\n",
        "        color=['red', 'blue', 'orange', 'green'])\n",
        "plt.title(\"Average Neutral Rewards vs. Medians\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.ylim(min(average_sentiment_score_old, average_sentiment_score_new, median_sentiment_score_old, median_sentiment_score_new) - 0.1,\n",
        "         max(average_sentiment_score_old, average_sentiment_score_new, median_sentiment_score_old, median_sentiment_score_new) + 0.1)\n",
        "\n",
        "# Cola scores\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.bar([\"Old Mean\", \"New Mean\", \"Old Median\", \"New Median\"],\n",
        "        [average_cola_score_old, average_cola_score_new, median_cola_score_old, median_cola_score_new],\n",
        "        color=['red', 'blue', 'orange', 'green'])\n",
        "plt.title(\"Average Cola Rewards vs. Medians\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.ylim(min(average_cola_score_old, average_cola_score_new, median_cola_score_old, median_cola_score_new) - 0.1,\n",
        "         max(average_cola_score_old, average_cola_score_new, median_cola_score_old, median_cola_score_new) + 0.1)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
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      "metadata": {
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      "execution_count": null,
      "outputs": []
    }
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